Use of the Correlation Coefficient for Rotating Machine Monitoring

The use of electronics and computer technology in production systems has greatly improved the quality of our industrial products. The productivity of these installations is a function of the maintenance quality applied to the equipment. Several methods are used to monitor the functioning of industrial installations. One of these methods is vibration analysis. The vibration signals from the rotating machines support several types of information related to the working state of the production tool. The processing of this information makes it possible to have decision tools for maintenance. In this work, we propose a method of anticipating the maintenance of rotating machines. The algorithm we propose starts with the removal of 512 point windows during the running time of the ball bearing. Each signal is decomposed by DWT: we obtain the approximation coefficients. These coefficients make it possible to determine the correlation coefficient between the so-called reference window and the other windows following the functioning of the ball bearing. The correlation coefficient is then the fundamental element of the decision. This algorithm has been applied to real vibration data and the results are encouraging.

An Automated End-to-End Side Channel Analysis Based on Probabilistic Model

In this paper, we propose a new automated way to find out the secret exponent from a single power trace. We segment the power trace into subsignals that are directly related to recovery of the secret exponent. The proposed approach does not need the reference window to slide, templates nor correlation coefficients compared to previous manners. Our method detects change points in the power trace to explore the locations of the operations and is robust to unexpected noise addition. We first model the change point detection problem to catch the subsignals irrelevant to the secret and solve this problem with Markov Chain Monte Carlo (MCMC) which gives a global optimal solution. After separating the relevant and irrelevant parts in signal, we extract features from the segments and group segments into clusters to find the key exponent. Using single power trace indicates the weakest power level of attacker where there is a very slight chance of acquiring as many power traces as needed for breaking the key. We empirically show the improvement in accuracy even with presence of high level of noise.

AN EMBEDDED SYSTEM ARCHITECTURE OF AUTOMATIC CENSORED ORDERED STATISTIC DETECTOR TECHNIQUES

We designed and tested a novel field-programmable gate array (FPGA)-based embedded system that uses automatic censored ordered statistics detector (ACOSD) algorithms to detect targets in clutter with lognormal distribution. The detection process operates through two techniques called backward and forward ACOSD (B-ACOSD and F-ACOSD, respectively), which work in parallel to increase the detection accuracy and reduce the false alarm rate. Two architectures were considered for the proposed detector. The B-ACOSD algorithm operates the censoring beginning from the last cell belonging to a window of N range cells, whereas the F-ACOSD algorithm considers the censoring based on a scan beginning with the first cell in the same sorted window of cells. The detector is implemented on a FPGA-Altera Stratix II as a system-on-chip that integrates a Nios II core processor with our proposed detector as a co-processor and additional embedded memories and interfaces using parallelism and pipelining. For a reference window of 16 cells, the processor works properly with a processing speed of up to 129.13 MHz and a processing time of only 0.23 μs, within the range of the maximum tolerated delay of 0.5 μs fixed by the pulse width [A. Farina, A. Russo and F. A. Studer, IEE Proc. F Commun. Radar Signal Process.133 (1986) 39–54] for viewing a target at high resolution.

A New Adaptive CFAR Detector

A new CFAR detector based on Grubbs statistic called G-CFAR detector is proposed and its detection architecture is given.in this paper. Also the values of the threshold in different confidence level are given. The G-CFAR is evaluated through Monte-Carlo simulation in various environments. In homogeneous background, the performance of G-CFAR shows a little worse than that of CA-CFAR. In multiple targets background, the G-CFAR performs robustly, especially when the interfering targets are not confined to a single half of the reference window, the G-CFAR keeps good performance, while the VI-CFAR can not detect the targets effectively .